• Train AI models with Unsloth and Hugging Face Jobs for FREE This blog post covers how to use Unsloth and Hugging Face Jobs for fast LLM fine-tuning (specifically LiquidAI/LFM2.5-1.2B-Instruct ) through coding agents like Claude Code and Codex. • Unsloth provides ~2x faster training and ~60% less VRAM usage compared to standard methods, so training small models can cost just a few dollars. • Small language models like LFM2.5-1.2B-Instruct are ideal candidates for fine-tuning. • They are cheap to train, fast to iterate on, and increasingly competitive with much larger models on focused tasks. • LFM2.5-1.2B-Instruct runs under 1GB of memory and is optimized for on-device deployment, so what you fine-tune can be served on CPUs, phones, and laptops. • You will need We are giving away free credits to fine-tune models on Hugging Face Jobs.

Article Summaries:

  • A new guide shows how to fine‑tune small language models for free using Unsloth and Hugging Face Jobs. Unsloth accelerates training by roughly 2× and cuts VRAM usage by 60 %, making models such as LiquidAI/LFM2.5‑1.2B‑Instruct (under 1 GB) inexpensive to iterate on. Hugging Face offers free credits and a one‑month Pro subscription to its Jobs platform, allowing users to submit training jobs via the hf‑cli or through coding‑agent skills (Claude Code, Codex). The post details setup steps, required credentials, and how to install the Hugging Face model‑trainer skill for automated script generation and job monitoring.

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